LogAnalyzer MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but 'analyze_log' and 'rapid_debug' could be confused as both involve analyzing logs for debugging, with 'rapid_debug' emphasizing speed and actionable fixes. The other tools like 'list_watched_files', 'stop_watching', and 'watch_log_file' are clearly distinct for file monitoring management.
Naming Consistency3/5
The naming is mixed with some consistent patterns like verb_noun ('analyze_log', 'watch_log_file', 'stop_watching') but deviations such as 'get_recent_errors' and 'list_watched_files' use different verb styles, and 'quick_scan' and 'rapid_debug' include emojis and marketing language, breaking consistency.
Tool Count5/5
With 7 tools, the count is well-scoped for a log analysis server, covering core operations like analysis, monitoring, and file management without being excessive or too sparse, fitting typical MCP server ranges.
Completeness4/5
The tool set covers key log analysis workflows including monitoring (watch/stop/list), analysis (analyze/scan/debug), and error retrieval, but lacks explicit tools for configuration management or historical data export, which could be minor gaps in a comprehensive logging system.
Average 3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository is licensed under MIT License.
This repository includes a README.md file.
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{
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  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't add context beyond that—e.g., it doesn't mention if this is a read-only operation, what permissions are needed, how errors are formatted, or any rate limits. This leaves significant gaps for a tool that likely interacts with monitored files.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of error analysis and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'error analysis' entails, the format of returned data, or how it relates to sibling tools like 'analyze_log'. For a tool with no structured behavioral hints, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters ('filePath' and 'limit'). The description doesn't add any meaning beyond what the schema provides, such as explaining what 'recent' means or how errors are prioritized. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('recent error analysis from monitored files'), making the purpose understandable. It doesn't explicitly distinguish from siblings like 'analyze_log' or 'quick_scan', which might have overlapping functionality, so it doesn't reach the highest score for sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_log' or 'list_watched_files'. It lacks context about prerequisites, such as whether files need to be monitored first, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It claims 'Instant analysis with actionable fixes and debug commands' and 'under 30 seconds,' which hints at performance and output type, but doesn't detail what 'actionable fixes' entail, whether it modifies logs, requires specific permissions, or handles errors. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with key information ('DEBUG SERVER LOGS IN UNDER 30 SECONDS'), using an emoji and bold claims efficiently. It consists of a single sentence that communicates the core purpose and speed, though it could be slightly more structured by separating features from promises.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (debugging logs with potential fixes), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'actionable fixes' or 'debug commands' mean in practice, the return format, or error handling. For a tool that implies analysis and possible actions, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with 'logText' documented as 'Log content to rapidly analyze and debug.' The description adds no additional parameter semantics beyond this, such as format constraints or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'DEBUG SERVER LOGS' with 'Instant analysis with actionable fixes and debug commands.' It specifies the verb (debug/analyze) and resource (server logs), though it doesn't explicitly differentiate from sibling tools like 'analyze_log' or 'get_recent_errors.' The description is specific but lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_log' or 'get_recent_errors.' It mentions 'rapidly analyze and debug' but doesn't specify contexts, exclusions, or prerequisites. Without explicit when/when-not instructions, it offers minimal usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't describe what happens after stopping (e.g., whether monitoring is permanently halted, if it can be restarted, or if there are side effects like freeing resources). For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with zero waste. It is front-loaded with the core action and resource, making it highly efficient and easy to parse. Every word earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's mutation nature (stopping monitoring implies a state change) and lack of annotations or output schema, the description is incomplete. It doesn't cover behavioral aspects like what constitutes successful execution, error conditions, or return values. For a tool that likely interacts with a monitoring system, more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'filePath' clearly documented in the schema as 'Path to the log file to stop monitoring'. The description adds no additional meaning beyond this, as it doesn't elaborate on path formats, validation, or examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Stop monitoring') and the resource ('a specific log file'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'watch_log_file' (which would be the inverse) or 'list_watched_files', but the verb 'stop' implies a complementary relationship rather than direct competition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., that the file must already be watched), nor does it reference sibling tools like 'watch_log_file' for starting monitoring or 'list_watched_files' to check current watches. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'real-time error detection' which implies ongoing monitoring, but fails to describe critical behaviors such as whether this starts a background process, how errors are reported, if it requires specific permissions, or what happens on tool invocation. This leaves significant gaps for a monitoring tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that efficiently conveys the core action and purpose without any wasted words. It is appropriately front-loaded with the main verb and resource, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a monitoring tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a stream, status, or error messages), how long monitoring persists, or interaction with sibling tools like 'stop_watching'. Given the complexity and lack of structured data, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters ('filePath' and 'pollInterval'). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Start monitoring') and resource ('a log file'), and specifies the goal ('for real-time error detection'). However, it doesn't explicitly distinguish this tool from its siblings like 'list_watched_files' or 'stop_watching', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'analyze_log', 'get_recent_errors', or 'quick_scan'. It lacks any mention of prerequisites, exclusions, or comparative contexts, leaving the agent with insufficient direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'AI-powered insights', hinting at generative capabilities, but fails to detail critical aspects like processing time, rate limits, authentication needs, or what constitutes 'root cause analysis' (e.g., whether it modifies data or is read-only). For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Analyze error logs and provide root cause analysis with AI-powered insights'. It is front-loaded with the core purpose and avoids unnecessary words, making it easy to parse quickly. Every part of the sentence contributes meaning without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (AI analysis with 3 parameters) and lack of annotations and output schema, the description is minimally adequate but incomplete. It states the purpose concisely but omits behavioral details, usage context, and output expectations. For a tool that performs analysis, more information on results or limitations would enhance completeness, though it meets a basic threshold.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, providing clear details for all three parameters ('logText', 'contextLines', 'logFormat'). The description adds no additional parameter semantics beyond what's in the schema, such as explaining how 'AI-powered insights' relate to the parameters. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze error logs and provide root cause analysis with AI-powered insights'. It specifies the verb ('analyze'), resource ('error logs'), and outcome ('root cause analysis with AI-powered insights'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_recent_errors' or 'rapid_debug', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'get_recent_errors', 'quick_scan', and 'rapid_debug' available, there's no indication of specific contexts, prerequisites, or exclusions for using 'analyze_log'. This lack of comparative guidance leaves the agent uncertain about tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('List') but doesn't describe what 'currently monitored' entails, whether this is a read-only operation, potential rate limits, or the format of the returned list. This leaves significant gaps for a tool that interacts with system resources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. However, it lacks details on behavioral aspects like what 'monitored' means or the return format, which could be important for an agent to use it correctly in context with sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately doesn't add parameter details, as there are none to document, aligning with the baseline for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all currently monitored log files'), providing a specific purpose. However, it doesn't explicitly differentiate from sibling tools like 'get_recent_errors' or 'analyze_log', which might also involve log file operations but with different scopes or functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'get_recent_errors' or 'analyze_log'. It lacks context on prerequisites, timing, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions performance (< 1 second) which is useful behavioral context, but lacks critical details: whether it's read-only or mutative, error handling, rate limits, or what 'scan' entails (e.g., pattern matching, simple keyword search). The speed claim is helpful but insufficient for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key information: speed, purpose, and performance metric. Every word earns its place with no redundancy or fluff, making it optimally concise for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 1 parameter with full schema coverage and no output schema, the description provides adequate context for a simple scanning tool. However, with no annotations and behavioral gaps (e.g., unspecified return format, error handling), it's minimally complete but leaves the agent guessing about operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter 'logText' as 'Log content for quick error detection'. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function as 'Ultra-fast log scan for real-time monitoring' with a specific performance metric (< 1 second). It distinguishes from siblings like 'analyze_log' by emphasizing speed rather than depth, but doesn't explicitly contrast with all alternatives like 'get_recent_errors' or 'rapid_debug'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for 'real-time monitoring' and 'quick error detection' (via parameter description), suggesting when speed is critical. However, it doesn't provide explicit guidance on when to choose this over siblings like 'analyze_log' (depth vs speed) or 'get_recent_errors' (historical vs current).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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